Adipokines and genetic factors in overweight or obese but metabolically healthy Polish women. 2010

Wojciech Bik, and Jerzy Ostrowski, and Agnieszka Baranowska-Bik, and Ewa Wolinska-Witort, and Magdalena Bialkowska, and Lidia Martynska, and Boguslawa Baranowska
Neuroendocrinology Department, Medical Centre of Postgraduate Education, Marymoncka 99/103, 01-813 Warsaw, Poland. zncmkp@op.pl

OBJECTIVE Obesity may be accompanied by enhanced metabolic disturbances but not all obese patients suffer from metabolic syndrome. Since metabolic homeostasis is under control of genetic factors underlying expression of adipokines, we aimed to compare the serum concentrations of adiponectin and resistin, and polymorphism in their genes, in overweight or obese Polish women. METHODS The study included 265 women with BMI above 25 kg/m2 (140 metabolically healthy and 125 with metabolic syndrome) and 104 non-obese women as a control group. Anthropometric parameters (BMI, BIA, WHR), blood pressure, lipid, glucose and HOMA-IR profiles as well as serum concentrations of adiponectin, HMW adiponectin and resistin were evaluated. Gene polymorphisms of adiponectin gene (276G/T; 11377C/G; 11391G/A) and resistin gene (420C/G; 62G/A; 537A/C) were analyzed using TaqMan SNP genotyping assays. RESULTS Higher serum concentrations of total adiponectin and lower levels of resistin were found in metabolically healthy patients when compared to those diagnosed with metabolic syndrome. No differences of serum HMW and resistin concentrations were observed between overweight or obese but metabolically healthy subjects and normal weight controls. No associations of investigated polymorphisms and the presence of metabolic syndrome were noticed in overweight/obese women with metabolic syndrome. CONCLUSIONS The assessment of total adiponectin in sera seems to be promising target in distinguishing subjects with obesity who undergo a diagnostic procedure for metabolic syndrome. Moreover, the evaluation of adipokine array may help to select patients with higher risk of metabolic disturbances that are associated with severe diseases.

UI MeSH Term Description Entries
D007333 Insulin Resistance Diminished effectiveness of INSULIN in lowering blood sugar levels: requiring the use of 200 units or more of insulin per day to prevent HYPERGLYCEMIA or KETOSIS. Insulin Sensitivity,Resistance, Insulin,Sensitivity, Insulin
D008875 Middle Aged An adult aged 45 - 64 years. Middle Age
D009765 Obesity A status with BODY WEIGHT that is grossly above the recommended standards, usually due to accumulation of excess FATS in the body. The standards may vary with age, sex, genetic or cultural background. In the BODY MASS INDEX, a BMI greater than 30.0 kg/m2 is considered obese, and a BMI greater than 40.0 kg/m2 is considered morbidly obese (MORBID OBESITY).
D011044 Poland A country in central Europe, east of Germany. The capital is Warsaw. Polish People's Republic,Republic of Poland
D011110 Polymorphism, Genetic The regular and simultaneous occurrence in a single interbreeding population of two or more discontinuous genotypes. The concept includes differences in genotypes ranging in size from a single nucleotide site (POLYMORPHISM, SINGLE NUCLEOTIDE) to large nucleotide sequences visible at a chromosomal level. Gene Polymorphism,Genetic Polymorphism,Polymorphism (Genetics),Genetic Polymorphisms,Gene Polymorphisms,Polymorphism, Gene,Polymorphisms (Genetics),Polymorphisms, Gene,Polymorphisms, Genetic
D005260 Female Females
D005838 Genotype The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS. Genogroup,Genogroups,Genotypes
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000328 Adult A person having attained full growth or maturity. Adults are of 19 through 44 years of age. For a person between 19 and 24 years of age, YOUNG ADULT is available. Adults
D016015 Logistic Models Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor. Logistic Regression,Logit Models,Models, Logistic,Logistic Model,Logistic Regressions,Logit Model,Model, Logistic,Model, Logit,Models, Logit,Regression, Logistic,Regressions, Logistic

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